Live migration is not supported by current Intel® SGX implementations. So, software emulation is unavoidable to enable deployed hypervisors migrating live virtual machines running SGX enclaves in the cloud. However, copying the running state of an enclave requires read/write of enclave's memory from outside, which is impossible. Therefore, software-only mechanisms, as opposed to hardware extensions, are not transparent to the virtual machines, and need to deduce some hardware-defined metadata by emulation. Also, they need to synchronise enclaves' stop/resume between two remote platforms. In this paper, we formally verify the only solution proposed in the literature that uses such a mechanism, in which we identify a severe critical situation where the whole live migration process gets stuck. Moreover and due to a specification flaw, we determine a condition that leads to the violation of instance uniqueness of enclaves. That may induce vulnerabilities for fork and rollback attacks. To remedy to these design shortcomings, we propose some easy-to-implement solutions to remove deadlock situations and ensure state uniqueness at the expense of a longer downtime.

In this paper, we apply model-checking approach to formally analyze energy consumption of the radio interface in order to guarantee network lifetime in the context of the Internet of Things. We propose a joint MAC-physical model of the IEEE 802.15.4 standard to capture and represent key operations that consume energy resources of the nodes at the radio interface component. We argue that the combination of the radio interface ON/OFF state switching mechanism with CSMA/CA medium access method leads to better modeling of the energy consumption and help understanding the interaction between MAC and physical layers. Our model provides accurate representation of simulation models at the first two layers of node’s protocol stack compliant with IEEE 802.15.4 standard.

Fault-tolerance is an important function in target tracking application using wireless sensor networks. We propose in this paper, an efficient fault-tolerant approach for target tracking that prevents the loss of the target. Instead of using a single prediction mechanism, our approach uses a multi-level incremental prediction technique that adjusts the prediction precision of the target movement. The responsible node of target detection uses multiple historical information pieces to calculate multi-level predictions which have different precision levels according to the number of information pieces used. Thanks to our parametric prediction model, our approach increases the prediction success rate and decreases the target loss frequency compared to basic approaches that use simple prediction models.

Abstract Reactive routing protocols for mobile ad hoc networks reduce the routing cost in high mobility environments where link failures are frequent. However, route discovery in these protocols is typically performedvianetwork‐wide flooding, which consumes a substantial amount of bandwidth and causes a significant latency to data packets. To improve the dynamic source routing (DSR) protocol and overcome these limitations, we propose two optimization techniques,viz. generalized salvaging mechanism and cache maintenance using a distributed topology discovery mechanism through mobile ant‐agents. We show, by simulations, that our contributions improve the DSR performance, and particularly in large scale networks with high mobility and heavy load that cause frequent link failures. Copyright © 2008 John Wiley & Sons, Ltd.